Imaging canopy temperature: shedding (thermal) light on ecosystem processes

Genomic signals of ecogeographic adaptation in a wild relative are associated with improved wheat performance under drought stress

BACKGROUND

Prioritizing wild relative diversity for improving crop adaptation to emerging drought-prone environments is challenging. Here, we combine the genome-wide environmental scans (GWES) in wheat diploid ancestor Aegilops tauschii (Ae. tauschii) with allele testing in the genetic backgrounds of adapted cultivars to identify diversity for improving wheat adaptation to water-limiting conditions.

RESULTS

We evaluate the adaptive allele effects in Ae. tauschii-wheat introgression lines phenotyped for multiple traits under irrigated and water-limiting conditions using both unmanned aerial system-based imaging and conventional approaches. The GWES show that climatic gradients alone explain more than half of genomic variation in Ae. tauschii, with many alleles associated with climatic factors in Ae. tauschii being linked with improved performance of introgression lines under water-limiting conditions. We find that the most significant GWES signals associated with temperature annual range in the wild relative are linked with reduced canopy temperature in introgression lines and increased yield.

CONCLUSIONS

Our results suggest that introgression of climate-adaptive alleles from Ae. tauschii has the potential to improve wheat performance under water-limiting conditions, and that variants controlling physiological processes responsible for maintaining leaf temperature are likely among the targets of adaptive selection in a wild relative. Adaptive variation uncovered by GWES in wild relatives has the potential to improve climate resilience of crop varieties.

Modeling the recent drought and thinning impacts on energy, water and carbon fluxes in a boreal forest

Globally, boreal forests act as important carbon sinks, however, drought and forest management could substantially alter the sink strength, though the controlling mechanisms of drought and management remain unclear. In this study, we combined the detailed process-based CoupModel with multiple measurements to study the impacts of recent drought and forest thinning on a boreal forest during 2018-2021. CoupModel after calibration showed high ability to represent the dynamics of long-term net ecosystem exchange and its responses to environmental changes. The model simulation showed that the canopy temperature exacerbated the dominant role in regulating the boreal forest growth during the 2018 extreme drought year with slight increase in the annual mean net carbon uptake by 76.65 g C/m2/yr compared to 2017. The posterior model simulations ensemble suggested that thinning of trees in 2019-2020 caused the boreal forest in 2020 to be a sink to slight source ([-229.95, 94.90] g C/m2/yr, 90 % confidence interval), while the observations depicted a small source (69.35 g C/m2/yr). Moreover, rapid recovery of the boreal forest to a carbon sink was found in 2021, though remaining smaller than the carbon sink in 2017. Overall, the negative impacts from drought and harvest (2018-2021) were found to have offset the positive impacts from climate by 8 % - 92 %, on the net carbon uptake. This study highlights the resilience of boreal forests as carbon sink and provides new insights into the boreal forests' responses to both climate change and management.

On the importance of vapor pressure deficit for the determination of the photosynthetic temperature optimum in tropical trees

This article is a Commentary on Slot et al. (2024), 244: 1238–1249.

Canopy temperatures strongly overestimate leaf thermal safety margins of tropical trees

Current estimates of temperature effects on plants mostly rely on air temperature, although it can significantly deviate from leaf temperature (Tleaf). To address this, some studies have used canopy temperature (Tcan). However, Tcan fails to capture the fine-scale variation in Tleaf among leaves and species in diverse canopies. We used infrared radiometers to study Tleaf and Tcan and how they deviate from air temperature (ΔTleaf and ΔTcan) in multispecies tropical tree plantations at three sites along an elevation and temperature gradient in Rwanda. Our results showed high Tleaf (up to c. 50°C) and ΔTleaf (on average 8-10°C and up to c. 20°C) of sun-exposed leaves during 10:00 h-15:00 h, being close to or exceeding photosynthetic heat tolerance thresholds. These values greatly exceeded simultaneously measured values of Tcan and ΔTcan, respectively, leading to strongly overestimated leaf thermal safety margins if basing those on Tcan data. Stomatal conductance and leaf size affected Tleaf and Tcan in line with their expected influences on leaf energy balance. Our findings highlight the importance of leaf traits for leaf thermoregulation and show that monitoring Tcan is not enough to capture the peak temperatures and heat stress experienced by individual leaves of different species in tropical forest canopies.

Nighttime warming and nitrogen addition effects on the microclimate of a freshwater wetland dominated by Phragmites australis

The critical impacts of microclimate on carbon (C) cycling have been widely reported. However, the potential effects of global change on wetland microclimate remain unclear, primarily because of the absence of field manipulative experiment in inundated wetland. This study was designed to examine the effects of nighttime warming and nitrogen (N) addition on air, water, and sediment temperature and also reveal the controlling factors in a Phragmites australis dominated freshwater wetland on the North China Plain. Nighttime warming increased daily air, water, and sediment temperature by 0.24 °C, 0.27 °C, and 0.36 °C, respectively. The diurnal temperature range of water was decreased by 0.44 °C under nighttime warming, whereas warming had no effect on diurnal temperature range of air and sediment. In addition, N addition caused a reduction of 0.20 °C and 0.14 °C in daily water and sediment temperature by increasing vegetation coverage. There was a significant interaction between nighttime warming and N addition on water temperature. Furthermore, the vapor pressure deficit is the main factor affecting the extent of the warming-induced increases in air temperature. The changes of height and leaf area index of Phragmites australis are responsible for the cooling effects in the N addition plots. This study provides empirical evidence for the positive climate warming - microclimate feedback in freshwater wetland. However, N deposition leads to decreased water and sediment temperature. Our findings highlight the importance of incorporating the differential impacts of nighttime warming and N addition on air, water, and sediment temperature into the predictions of wetland C cycling responses to climate change.

Spring wood phenology responds more strongly to chilling temperatures than bud phenology in European conifers

A comparative assessment of bud and wood phenology could aid a better understanding of tree growth dynamics. However, the reason for asynchronism or synchronism in leaf and cambial phenology remains unclear. To test the assumption that the temporal relationship between the budburst date and the onset date of wood formation is due to their common or different responses to environmental factors, we constructed a wood phenology dataset from previous literature, and compared it with an existing bud phenology dataset in Europe. We selected three common conifers (Larix decidua Mill., Picea abies (L.) H. Karst. and Pinus sylvestris L.) in both datasets and analyzed 909 records of the onset of wood formation at 47 sites and 238,720 records of budburst date at 3051 sites. We quantified chilling accumulation (CA) and forcing requirement (FR) of budburst and onset of wood formation based on common measures of CA and FR. We then constructed negative exponential CA-FR curves for bud and wood phenology separately. The results showed that the median, variance and probability distribution of CA-FR curves varied significantly between bud and wood phenology for three conifers. The different FR under the same chilling condition caused asynchronous bud and wood phenology. Furthermore, the CA-FR curves manifested that wood phenology was more sensitive to chilling than bud phenology. Thus, the FR of the onset of wood formation increases more than that of budburst under the same warming scenarios, explaining the stronger earlier trends in the budburst date than the onset date of woody formation simulated by the process-based model. Our work not only provides a possible explanation for asynchronous bud and wood phenology from the perspective of organ-specific responses to chilling and forcing, but also develops a phenological model for predicting both bud and wood phenology with acceptable uncertainties.

Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue

This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.

Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses

Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.

Thermography captures the differential sensitivity of dryland functional types to changes in rainfall event timing and magnitude

Drylands of the southwestern United States are rapidly warming, and rainfall is becoming less frequent and more intense, with major yet poorly understood implications for ecosystem structure and function. Thermography-based estimates of plant temperature can be integrated with air temperature to infer changes in plant physiology and response to climate change. However, very few studies have evaluated plant temperature dynamics at high spatiotemporal resolution in rainfall pulse-driven dryland ecosystems. We address this gap by incorporating high-frequency thermal imaging into a field-based precipitation manipulation experiment in a semi-arid grassland to investigate the impacts of rainfall temporal repackaging. All other factors held constant, we found that fewer/larger precipitation events led to cooler plant temperatures (1.4°C) compared to that of many/smaller precipitation events. Perennials, in particular, were 2.5°C cooler than annuals under the fewest/largest treatment. We show these patterns were driven by: increased and consistent soil moisture availability in the deeper soil layers in the fewest/largest treatment; and deeper roots of perennials providing access to deeper plant available water. Our findings highlight the potential for high spatiotemporal resolution thermography to quantify the differential sensitivity of plant functional groups to soil water availability. Detecting these sensitivities is vital to understanding the ecohydrological implications of hydroclimate change.

Detailed in situ leaf energy budget permits the assessment of leaf aerodynamic resistance as a key to enhance non-evaporative cooling under drought

The modulation of the leaf energy budget components to maintain optimal leaf temperature are fundamental aspects of plant functioning and survival. Better understanding these aspects becomes increasingly important under a drying and warming climate when cooling through evapotranspiration (E) is suppressed. Combining novel measurements and theoretical estimates, we obtained unusually comprehensive twig-scale leaf energy budgets under extreme field conditions in droughted (suppressed E) and non-droughted (enhanced E) plots of a semi-arid pine forest. Under the same high mid-summer radiative load, leaf cooling shifted from relying on nearly equal contributions of sensible (H) and latent (LE) energy fluxes in non-droughted trees to relying almost exclusively on H in droughted ones, with no change in leaf temperature. Relying on our detailed leaf energy budget, we could demonstrate that this is due to a 2× reduction in leaf aerodynamic resistance. This capability for LE-to-H shift in leaves of mature Aleppo pine trees under droughted field conditions without increasing leaf temperature is likely a critical factor in the resilience and relatively high productivity of this important Mediterranean tree species under drying conditions.

Absence of canopy temperature variation despite stomatal adjustment in Pinus sylvestris under multidecadal soil moisture manipulation

Global warming and droughts push forests closer to their thermal limits, altering tree carbon uptake and growth. To prevent critical overheating, trees can adjust their thermotolerance (Tcrit ), temperature and photosynthetic optima (Topt and Aopt ), and canopy temperature (Tcan ) to stay below damaging thresholds. However, we lack an understanding of how soil droughts affect photosynthetic thermal plasticity and Tcan regulation. In this study, we measured the effect of soil moisture on the seasonal and diurnal dynamics of net photosynthesis (A), stomatal conductance (gs ), and Tcan , as well as the thermal plasticity of photosynthesis (Tcrit , Topt , and Aopt ), over the course of 1 yr using a long-term irrigation experiment in a drought-prone Pinus sylvestris forest in Switzerland. Irrigation resulted in higher needle-level A, gs , Topt , and Aopt compared with naturally drought-exposed trees. No daily or seasonal differences in Tcan were observed between treatments. Trees operated below their thermal thresholds (Tcrit ), independently of soil moisture content. Despite strong Tcan and Tair coupling, we provide evidence that drought reduces trees' temperature optimum due to a substantial reduction of gs during warm and dry periods of the year. These findings provide important insights regarding the effects of soil drought on the thermal tolerance of P. sylvestris.

Tropical forests are approaching critical temperature thresholds

The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages approximately 46.7 °C (Tcrit)1. However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. Here we found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers and remote sensing (ECOSTRESS) have midday peak temperatures of approximately 34 °C during dry periods, with a long high-temperature tail that can exceed 40 °C. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper canopy leaves exceed Tcrit 0.01% of the time. Furthermore, upper canopy leaf warming experiments (+2, 3 and 4 °C in Brazil, Puerto Rico and Australia, respectively) increased leaf temperatures non-linearly, with peak leaf temperatures exceeding Tcrit 1.3% of the time (11% for more than 43.5 °C, and 0.3% for more than 49.9 °C). Using an empirical model incorporating these dynamics (validated with warming experiment data), we found that tropical forests can withstand up to a 3.9 ± 0.5 °C increase in air temperatures before a potential tipping point in metabolic function, but remaining uncertainty in the plasticity and range of Tcrit in tropical trees and the effect of leaf death on tree death could drastically change this prediction. The 4.0 °C estimate is within the 'worst-case scenario' (representative concentration pathway (RCP) 8.5) of climate change predictions2 for tropical forests and therefore it is still within our power to decide (for example, by not taking the RCP 6.0 or 8.5 route) the fate of these critical realms of carbon, water and biodiversity3,4.

Short- and long-term warming events on photosynthetic physiology, growth, and yields of field grown crops

Global temperatures are rising from increasing concentrations of greenhouse gases in the atmosphere associated with anthropogenic activities. Global warming includes a warmer shift in mean temperatures as well as increases in the probability of extreme heating events, termed heat waves. Despite the ability of plants to cope with temporal variations in temperature, global warming is increasingly presenting challenges to agroecosystems. The impact of warming on crop species has direct consequences on food security, therefore understanding impacts and opportunities to adapt crops to global warming necessitates experimentation that allows for modification of growth environments to represent global warming scenarios. Published studies addressing crop responses to warming are extensive, however, in-field studies where growth temperature is manipulated to mimic global warming are limited. Here, we provide an overview of in-field heating techniques employed to understand crop responses to warmer growth environments. We then focus on key results associated with season-long warming, as expected with rising global mean temperatures, and with heat waves, as a consequence of increasing temperature variability and rising global mean temperatures. We then discuss the role of rising temperatures on atmospheric water vapor pressure deficit and potential implications for crop photosynthesis and productivity. Finally, we review strategies by which crop photosynthetic processes might be optimized to adapt crops to the increasing temperatures and frequencies of heat waves. Key findings from this review are that higher temperatures consistently reduce photosynthesis and yields of crops even as atmospheric carbon dioxide increases, yet potential strategies to minimize losses from high-temperature exist.

It's only natural: Plant respiration in unmanaged systems

Respiration plays a key role in the terrestrial carbon cycle and is a fundamental metabolic process in all plant tissues and cells. We review respiration from the perspective of plants that grow in their natural habitat and how it is influenced by wide-ranging elements at different scales, from metabolic substrate availability to shifts in climate. Decades of field-based measurements have honed our understanding of the biological and environmental controls on leaf, root, stem, and whole-organism respiration. Despite this effort, there remain gaps in our knowledge within and across species and ecosystems, especially in more challenging-to-measure tissues like roots. Recent databases of respiration rates and associated leaf traits from species representing diverse biomes, plant functional types, and regional climates have allowed for a wider-lens view at modeling this important CO2 flux. We also re-analyze published data sets to show that maximum leaf respiration rates (Rmax) in species from around the globe are related both to leaf economic traits and environmental variables (precipitation and air temperature), but that root respiration does not follow the same latitudinal trends previously published for leaf data. We encourage the ecophysiological community to continue to expand their study of plant respiration in tissues that are difficult to measure and at the whole plant and ecosystem levels to address outstanding questions in the field.

Does plant ecosystem thermoregulation occur? An extratropical assessment at different spatial and temporal scales

To what degree plant ecosystems thermoregulate their canopy temperature (T_c ) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability. With global datasets of T_c , air temperature (T_a ), and other environmental and biotic variables from FLUXNET and satellites, we tested the 'limited homeothermy' hypothesis (indicated by T_c & T_a regression slope < 1 or T_c  < T_a around midday) across global extratropics, including temporal and spatial dimensions. Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 or T_c  > T_a around midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, their T_c -T_a difference (ΔT) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site-mean ΔT exhibits larger variations than the slope indicator, suggesting ΔT is a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial-wide ΔT variation (0-6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%). These results demonstrate diverse thermoregulation patterns across global extratropics, with most ecosystems negating the 'limited homeothermy' hypothesis, but their thermoregulation still occurs, implying that slope < 1 or T_c  < T_a are not necessary conditions for plant thermoregulation.

Use of thermal imaging and the photochemical reflectance index (PRI) to detect wheat response to elevated CO2 and drought

The spectral-based photochemical reflectance index (PRI) and leaf surface temperature (Tleaf ) derived from thermal imaging are two indicative metrics of plant functioning. The relationship of PRI with radiation-use efficiency (RUE) and Tleaf with leaf transpiration could be leveraged to monitor crop photosynthesis and water use from space. Yet, it is unclear how such relationships will change under future high carbon dioxide concentrations ([CO2 ]) and drought. Here we established an [CO2 ] enrichment experiment in which three wheat genotypes were grown at ambient (400 ppm) and elevated (550 ppm) [CO2 ] and exposed to well-watered and drought conditions in two glasshouse rooms in two replicates. Leaf transpiration (Tr ) and latent heat flux (LE) were derived to assess evaporative cooling, and RUE was calculated from assimilation and radiation measurements on several dates along the season. Simultaneous hyperspectral and thermal images were taken at~ $\unicode{x0007E}$ 1.5 m from the plants to derive PRI and the temperature difference between the leaf and its surrounding air (∆ $\unicode{x02206}$ Tleaf-air ). We found significant PRI and RUE and∆ $\unicode{x02206}$ Tleaf-air and Tr correlations, with no significant differences among the genotypes. A PRI-RUE decoupling was observed under drought at ambient [CO2 ] but not at elevated [CO2 ], likely due to changes in photorespiration. For a LE range of 350 W m-2 , the ΔTleaf-air range was~ $\unicode{x0007E}$ 10°C at ambient [CO2 ] and only~ $\unicode{x0007E}$ 4°C at elevated [CO2 ]. Thicker leaves in plants grown at elevated [CO2 ] suggest higher leaf water content and consequently more efficient thermoregulation at high [CO2 ] conditions. In general, Tleaf was maintained closer to the ambient temperature at elevated [CO2 ], even under drought. PRI, RUE, ΔTleaf -air , and Tr decreased linearly with canopy depth, displaying a single PRI-RUE and ΔTleaf -air  Tr model through the canopy layers. Our study shows the utility of these sensing metrics in detecting wheat responses to future environmental changes.

No evidence of canopy-scale leaf thermoregulation to cool leaves below air temperature across a range of forest ecosystems

Understanding and predicting the relationship between leaf temperature (T_leaf) and air temperature (T_air) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime T_leaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below T_air at higher temperatures (i.e., > ∼25-30°C) leading to slopes <1 in T_leaf/T_air relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature (T_can) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to T_can/T_air slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the T_can/T_air relationship. Canopy structure also plays an important role in T_can dynamics. Future climate warming is likely to lead to even greater T_can, with attendant impacts on forest carbon cycling and mortality risk.

Conceptualizing the 3D niche and vertical space use

Spatial partitioning in ecological communities has predominantly been described in two dimensions, yet habitat is complex and 3D. Complex space use mediates community structure and interaction strength by expanding spatial, temporal, and dietary dimensions. Vertical stratification of resources provides opportunities for novel specializations, creating a 3D niche. Competition and predation are mediated by 3D space use, as individuals use the vertical axis to access prey, flee predators, or avoid competitors. The 3D niche is important for long-term conservation strategies as species must navigate tradeoffs in habitat use between strata-specific threats and suboptimal habitat patches. Ultimately, elucidating the 3D niche has implications for protected area management and corridor design that directly influence species persistence and ecosystem function in a rapidly changing world.

Plant hydraulics, stomatal control, and the response of a tropical forest to water stress over multiple temporal scales

Many tropical regions are experiencing an intensification of drought, with increasing severity and frequency. The ecosystem response to these changes is still highly uncertain. On short time scales (from diurnal to seasonal), tropical forests respond to water stress by physiological controls, such as stomatal regulation and phenological adjustment, to cope with increasing atmospheric water demand and reduced water supply. However, the interactions among biological processes and co-varying environmental factors that determine the ecosystem-level fluxes are still unclear. Furthermore, climate variability at longer time scales, such as that generated by ENSO, produces less predictable effects because it depends on a highly stochastic combination of factors that might vary among forests and even between events in the same forest. This study will present some emerging patterns of response to water stress from 5 years of water, carbon, and energy fluxes observed on a seasonal tropical forest in central Panama, including an increase in productivity during the 2015 El Niño. These responses depend on the combination of environmental factors experienced by the forest throughout the seasonal cycle, in particular, increase in solar radiation, stimulating productivity, and increasing vapor pressure deficit (VPD) and decreasing soil moisture, limiting stomata opening. These results suggest a critical role of plant hydraulics in mediating the response to water stress over a broad range of temporal scales (diurnal, intraseasonal, seasonal, and interannual), by acclimating canopy conductance to light and VPD during different soil moisture regimes. A multilayer photosynthesis model coupled with a plant hydraulics scheme can reproduce these complex responses. However, results depend critically on parameters regulating water transport efficiency and the cost of water stress. As these costs have not been properly identified and quantified yet, more empirical research is needed to elucidate physiological mechanisms of hydraulic failure and recover, for example embolism repair and xylem regrowth.

Leaf morphological traits as adaptations to multiple climate gradients

Leaf morphological traits vary systematically along climatic gradients. However, recent studies in plant functional ecology have mainly analysed quantitative traits, while numerical models of species distributions and vegetation function have focused on traits associated with resource acquisition; both ignore the wider functional significance of leaf morphology.A dataset comprising 22 leaf morphological traits for 662 woody species from 92 sites, representing all biomes present in China, was subjected to multivariate analysis in order to identify leading dimensions of trait covariation (correspondence analysis), quantify climatic and phylogenetic contributions (canonical correspondence analysis with variation partitioning) and characterise co-occurring trait syndromes (k-means clustering) and their climatic preferences.Three axes accounted for >20% of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing-season temperature explained 8%-10% of trait variation; family 15%-32%. Microphyll or larger, mid- to dark green leaves with drip tips in wetter climates contrasted with nanophyll or smaller glaucous leaves without drip tips in drier climates. Thick, entire leaves in less seasonal climates contrasted with thin, marginal dissected, aromatic and involute/revolute leaves in more seasonal climates. Thick, involute, hairy leaves in colder climates contrasted with thin leaves with marked surface structures (surface patterning) in warmer climates. Distinctive trait clusters were linked to the driest and most seasonal climates, for example the clustering of picophyll, fleshy and succulent leaves in the driest climates and leptophyll, linear, dissected, revolute or involute and aromatic leaves in regions with highly seasonal rainfall. Several trait clusters co-occurred in wetter climates, including clusters characterised by microphyll, moderately thick, patent and entire leaves or notophyll, waxy, dark green leaves. Synthesis. The plastic response of size, shape, colour and other leaf morphological traits to climate is muted, thus their apparent shift along climate gradients reflects plant adaptations to environment at a community level as determined by species replacement. Information on leaf morphological traits, widely available in floras, could be used to strengthen predictive models of species distribution and vegetation function.

Thinning increases forest resiliency during unprecedented drought

Regional droughts are now widespread and are projected to further increase. Semi-arid ponderosa pine forests across the western USA, which occupy > 56 million ha, are experiencing unprecedented levels of drought due to the currently ongoing North American megadrought. Using unpiloted aerial vehicle (UAV) thermal images and ground-based hyperspectral data, here we show that ponderosa pine forest canopy temperatures increased during the 2021 summer drought up to 34.6 °C, far above a typical canopy temperature when ponderosa pine trees no longer uptake carbon. We infer that much of the western US ponderosa pine forests likely served as a net carbon source rather than a sink during the 2021 summer drought period. We also demonstrate that regional forest restoration thinning significantly reduced the drought impacts. Thinned ponderosa pine forests had significantly lower increase in canopy temperature and canopy water stress during the drought period compared to the non-thinned forest stands. Furthermore, our extensive soil moisture network data indicate that available soil moisture in the thinned forest was significantly greater at all soil depths of 25 cm, 50 cm, and 100 cm compared to the non-thinned forest, where soil moisture dry-down in the spring started significantly earlier and stayed dry for one month longer causing critical water stress for trees. Forest restoration thinning benefits that are otherwise unappreciated during average precipitation years are significantly amplified during unprecedented drought periods.

Downwelling longwave radiation and sensible heat flux observations are critical for surface temperature and emissivity estimation from flux tower data

Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. In contrast, plot-scale LST estimation at flux tower sites is commonly based on the inversion of upwelling longwave radiation captured by tower-mounted radiometers, whereas the role of the downwelling longwave radiation component is often ignored. We found that neglecting the reflected downwelling longwave radiation leads not only to substantial bias in plot-scale LST estimation, but also have important implications for the estimation of surface emissivity on which LST is co-dependent. The present study proposes a novel method for simultaneous estimation of LST and emissivity at the plot-scale and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both upwelling and downwelling longwave radiation components are 0.5–1.5 K lower than estimates using only upwelling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the upwelling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. The correspondence between plot-scale LST and landscape-scale LST depends on site-specific characteristics, such as canopy density, sensor locations and viewing angles. Here we also quantify the uncertainty in plot-scale LST estimates due to uncertainty in tower-based measurements using the different methods. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO₂ . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

'Dual-reference' method for high-precision infrared measurement of leaf surface temperature under field conditions

Temperature is a key control over biological activities from the cellular to the ecosystem scales. However, direct, high-precision measurements of surface temperature of small objects, such as leaves, under field conditions with large variations in ambient conditions remain rare. Contact methods, such as thermocouples, are prone to large errors. The use of noncontact remote-sensing methods, such as thermal infrared measurements, provides an ideal solution, but their accuracy has been low (c. 2°C) owing to the necessity for corrections for material emissivity and fluctuations in background radiation L_bg . A novel 'dual-reference' method was developed to increase the accuracy of infrared needle-leaf surface temperature measurements in the field. It accounts for variations in L_bg and corrects for the systematic camera offset using two reference plates. We accurately captured surface temperature and leaf-to-air temperature differences of needle-leaves in a forest ecosystem with large diurnal and seasonal temperature fluctuations with an uncertainty of ± 0.23°C and ± 0.28°C, respectively. Routine high-precision leaf temperature measurements even under harsh field conditions, such as demonstrated here, opens the way for investigating a wide range of leaf-scale processes and their dynamics.

Detecting Winter Cover Crops and Crop Residues in the Midwest US Using Machine Learning Classification of Thermal and Optical Imagery

Cover crops are an increasingly popular practice to improve agroecosystem resilience to climate change, pests, and other stressors. Despite their importance for climate mitigation and soil health, there remains an urgent need for methods that link winter cover crops with regional-scale climate mitigation and adaptation potential. Remote sensing is ideally suited to provide these linkages, yet, cover cropping has not been analyzed extensively in remote sensing research. Methods used for remote sensing of crops from satellites traditionally leverage the difference between visible and near-infrared reflectance to isolate the signal of photosynthetically active vegetation. However, using traditional greenness indices like the Normalized Difference Vegetation Index (NDVI) for remotely sensing winter vegetation, such as winter cover crops, is challenging because vegetation reflectance signals are often confounded with reflectance of bare soil and crop residues. Here, we present new and established methods of detecting winter cover crops using remote sensing observations. We find that remote sensing methods that incorporate thermal data in addition to traditional reflectance metrics are best able to distinguish between winter farm management practices. We conclude by addressing the potential of existing and upcoming hyperspectral and thermal missions to further assess agroecosystem function in the context of global change.